GSoC 2025 | UCSC OSPO

Scenic-RoboSuite Integration: Building the First Working Prototype

Mon, 29 Sep 2025 00:00:00 +0000

I’m Sahil, presenting the first working prototype of the Scenic-RoboSuite integration. This project is being mentored by Daniel Fremont and Eric Vin.

After months of development, we have achieved a functional prototype of the Scenic-RoboSuite interface. Researchers can now write basic declarative robotic manipulation scenarios in Scenic that execute with physics simulation in RoboSuite. While still in development, the prototype demonstrates the feasibility and potential of bridging probabilistic scenario generation with detailed robot control.

Major Achievements

MJCF XML Injection

The interface introduces direct MJCF XML support, allowing Scenic to build RoboSuite-native manipulable objects from raw XML definitions. Users can define custom objects with complex mesh geometries, textures, and physics properties directly in their Scenic scenarios:

dragon_xml = '''
<mujoco>
 <asset>
 <mesh file="dragon.stl" scale="0.01 0.01 0.01"/>
 <texture file="dragon_texture.png"/>
 </asset>
 <worldbody>
 <body name="object">
 <geom mesh="dragon_mesh" type="mesh"/>
 </body>
 </worldbody>
</mujoco>
'''

dragon = new CustomObject with mjcfXml dragon_xml

The system automatically handles collision geometry generation, joint creation for physics, and asset file resolution.

Complex Mesh Object Support

Import and manipulate arbitrary 3D models (STL, OBJ) with automatic mesh repair and texture mapping. The interface resolves file paths relative to Scenic files, copies assets to temporary directories for MuJoCo, and converts textures (JPG to PNG) when needed. This enables using custom robotic tools, industrial parts, or any 3D model in manipulation scenarios.

Custom Arena Definition

Define complete custom environments using MJCF XML, extending beyond RoboSuite’s built-in arenas:

custom_arena = new CustomArena with arenaXml localPath("warehouse.xml")

This allows creating specialized workspaces, factory floors, or research-specific environments while maintaining full physics simulation.

Multi-Robot Support

The interface handles multiple robots operating in the same workspace:

robot1 = new Panda at (-0.5, 0, 0)
robot2 = new UR5e at (0.5, 0, 0)
table = new Table at (0, 0, 0.425)

Each robot maintains independent control and can execute coordinated or individual behaviors.

Built-in Manipulation Behaviors

Ready-to-use behaviors for immediate testing and development:

MoveToPosition - Precise end-effector positioning
PickObject - Automated grasping with approach and closure
LiftToHeight - Controlled lifting to target heights
PickAndLift - Complete pick-and-place sequence

These behaviors use Operational Space Control (OSC) for intuitive 3D movement commands.

Extended Environment Configuration

The interface extends RoboSuite’s configurability through Scenic’s parameter system:

param controller_config = {'type': 'OSC_POSITION', 'impedance': 'low'}
param camera_view = 'robot0_eye_in_hand'
param lite_physics = True # Faster simulation for testing

Example: Probabilistic Pick-and-Place

model scenic.simulators.robosuite.model

# Randomly position cube on table
table = new Table at (0.6, 0, 0.425)
cube = new Box on table,
 with color (1, 0, 0, 1),
 with position (Uniform(-0.2, 0.2), Uniform(-0.2, 0.2), _)

# Robot adapts to random cube position
behavior AdaptivePickup():
 do PickAndLift(cube, height=1.1)

ego = new Panda at (0, 0, 0),
 with behavior AdaptivePickup()

Each scenario run generates a different cube position, testing the robot’s adaptive capabilities.

Challenges Overcome

Understanding Dual Architecture Paradigms

RoboSuite and Scenic operate on fundamentally different principles. RoboSuite builds environments imperatively through MuJoCo XML composition, expecting complete scene specification upfront. Scenic generates scenes probabilistically through constraint solving, requiring geometric knowledge before simulation. Bridging these required developing a two-pass system where we first extract geometry from a temporary RoboSuite environment, update Scenic’s understanding, then create the final simulation. This architectural mismatch touched every aspect of the integration, from object creation to property updates.

Discovering and Extending ManipulationEnv

RoboSuite’s documentation focuses on using pre-built tasks, not creating custom environments. Through extensive source code analysis, we discovered that ManipulationEnv was the key - it accepts robots as configuration while allowing customizable arenas and objects as components. This class became our foundation, but required significant extension. We implemented ScenicManipulationEnv to intercept Scenic’s object configurations, handle dynamic arena selection (EmptyArena vs MultiTableArena based on scene content), and manage the complex initialization sequence where robots, arenas, and objects must be assembled in specific order for MuJoCo compilation.

XML to 3D Mesh Pipeline

Converting MJCF XML to usable 3D meshes proved complex. MuJoCo uses XML to describe geometry, but Scenic needs actual mesh data for collision checking. We built a multi-stage pipeline: First, ElementTree parses the XML to extract mesh references and primitive definitions. Then, we handle two paths - for mesh files, we load STL/OBJ files with trimesh and apply XML-specified transformations; for primitives (boxes, cylinders), we generate meshes programmatically. The challenge intensified with composite objects - a table might have a box tabletop and four cylinder legs. We developed ComponentExtractor to analyze the MuJoCo scene graph, identify related geometries through naming patterns and hierarchy, and export each component as a separate GLB file with proper world transforms preserved.

File Path Resolution Discrepancies

Scenic and RoboSuite handle file paths completely differently. Scenic uses localPath() for paths relative to the scenario file, while RoboSuite expects paths relative to its package structure or absolute paths. MJCF XML compounds this - mesh references can be relative to the XML file location, not the calling code. We implemented a sophisticated path resolution system: detect whether paths come from embedded XML (relative to Scenic file) or external XML files (relative to XML location), copy all referenced assets (meshes, textures) to temporary directories accessible to MuJoCo, and handle texture format conversion (JPG to PNG) when needed. This system transparently manages assets whether they’re in the Scenic project, RoboSuite package, or absolute paths, making the interface truly portable.

Impact and Applications

This bridge enables:

Research: Generate diverse manipulation scenarios for robot learning algorithms
Testing: Validate robotic systems against probabilistic task variations
Development: Rapid prototyping of manipulation tasks without manual scene setup
Education: Teach robotics concepts through declarative scenario specification

The integration makes complex robotic simulations accessible through Scenic’s intuitive language while preserving RoboSuite’s detailed physics and control capabilities.

Documentation and Resources

The project includes:

example scenarios demonstrating all features
Comprehensive STATUS.md tracking working features and known issues
Technical documentation in docs/ covering architecture and troubleshooting
Mesh extraction utilities for pre-processing and caching

Current Status and Future Work

This prototype demonstrates that the Scenic-RoboSuite bridge is viable and functional. Basic features are working reliably:

Single-robot manipulation scenarios execute successfully
MJCF XML injection creates custom objects
Pick-and-place behaviors operate consistently
Multi-robot support functions in controlled scenarios

However, significant work remains:

Stability improvements: Some features work intermittently and need refinement
Velocity tracking: Full implementation awaits framework updates
Multi-robot coordination: Advanced synchronization primitives needed
Performance optimization: Mesh extraction and caching can be streamlined
Extended testing: More diverse scenarios and edge cases need validation

The prototype serves as a proof of concept, showing that probabilistic scenario specification can successfully drive physics-based robot simulation. The architecture is sound, the core features function, and the path forward is clear.

Conclusion

This working prototype of the Scenic-RoboSuite integration represents significant progress toward bridging probabilistic programming with robotic simulation. We’ve successfully demonstrated that declarative scenario specification can control detailed physics simulation, opening new possibilities for robotic system development and testing.

While not yet production-ready, the prototype provides a solid foundation for future development. Researchers can begin experimenting with basic manipulation scenarios, developers can test the interface with their use cases, and the community can contribute to making this bridge more robust and feature-complete.

The challenges overcome - from understanding dual architectures to implementing XML-to-mesh pipelines - have resulted in a functional system that validates our approach. This prototype proves that Scenic’s elegant scenario language and RoboSuite’s detailed physics can work together, setting the stage for a powerful new tool in robotics research and development.

Robot Manipulation with Scenic-RoboSuite

Wed, 30 Jul 2025 00:00:00 +0000

We’re Sahil, continuing work on the Scenic-RoboSuite integration for GSoC 2025. This project is mentored by Daniel Fremont and Eric Vin.

Since the last update, the Scenic-RoboSuite interface has made significant progress. The bidirectional bridge is now functional - robots can read sensor data and execute behaviors based on observations. However, these features are still in early stages and we’re working on making them more stable and consistent.

We’ve integrated RoboSuite’s Operational Space Control into Scenic. This control method lets you command the robot’s hand directly in 3D space (like “move 10cm left”) instead of calculating complex joint rotations. While the integration works, it’s rough around the edges and we’re currently focused on stabilizing it across different scenarios.

The main challenge was architectural - RoboSuite expects all robot commands bundled together each timestep, while Scenic processes them one by one. We solved this with a pending actions system that collects everything first, then executes in one go. Time synchronization was another challenge, matching Scenic’s steps with MuJoCo’s physics.

We’ve implemented a basic pick-and-place behavior for basic testing. The robot reads sensor data, calculates where to move, and adjusts continuously. It can successfully grasp and lift objects, though consistency varies between runs. The system supports three robot models and works with RoboSuite’s pre-built environments.

Custom world building is currently on hold. We’ve decided to focus on integrating existing RoboSuite features into Scenic first, then build Scenic’s capabilities like dynamic scenario randomization on top. For our first prototype, we’re aiming to extend the pick-and-place behavior into a full randomization demo - Scenic will randomly position the cube each run, and the robot will adapt to find and grasp it regardless of location.

The next two weeks focus on stabilizing current features and preparing this randomized scenario prototype. Expanding the behavior library and supporting additional environments will come in future phases after we have a solid foundation.

The core bridge between Scenic and RoboSuite is operational, but there’s significant work ahead to make it reliable and user-friendly.

AIDRIN Privacy-Centric Enhancements: Backend & UX Upgrades

Fri, 25 Jul 2025 00:00:00 +0000

⏱️ Reading time: 5–6 minutes

Hey everyone,

If you’ve ever wondered what it takes to make AI data pipelines not just smarter, but safer and more transparent, you’re in the right place. The last few weeks working on AIDRIN for GSoC have been a deep dive into the engine room of privacy and backend systems that power the AIDRIN project. My focus has been on building out the core privacy infrastructure and backend features that power AIDRIN’s ability to give users real, actionable insights about their data. It’s been challenging, sometimes messy, but incredibly rewarding to see these changes make a tangible difference.

Having Dr. Jean Luca Bez and Prof. Suren Byna as mentors, along with the support of the entire team, has truly made all the difference. Their guidance, encouragement, and collaborative spirit have been a huge part of this journey, whether I’m brainstorming new ideas or just trying to untangle a tricky bug.

Privacy Metrics: Making Data Safer

A major part of my work has been putting data privacy at the front and center in AIDRIN. I focused on integrating essential privacy metrics like k-anonymity, l-diversity, t-closeness, and more, making sure they’re not just theoretical checkboxes, but real tools that users can interact with and understand. Now, these metrics are fully wired up in the backend and visualized in AIDRIN, so privacy risks are no longer just a vague concern. They are something AI data preparers can actually see and act on. Getting these metrics to work seamlessly with different datasets and ensuring their accuracy took some serious backend engineering, but the payoff has been worth it.

Speeding Things Up (So You Don’t Have To Wait Around)

As AIDRIN started handling bigger datasets, some of the calculations can be time-consuming because data has to be accessed every time a metric is computed. To address this, I added caching for previously computed metrics, like class imbalance and privacy checks, and set up asynchronous execution with Celery and Redis. This should make the app super responsive. Rather than waiting for heavy computations to finish, one can start taking notes about other metrics or explore different parts of the app while their results are loading in the background. It’s a small change, but it helps keep the workflow moving smoothly.

Small Touch Ups That (Hopefully) Make a Big Difference

I also spent time on the details that make the app easier to use. Tooltips now explain what the privacy metrics actually mean, error messages are clearer, and there’s a new cache info page where you can see and clear your cached data. The sensitive attribute dropdown is less confusing now, especially if you’re working with quasi-identifiers. These tweaks might seem minor, but they add up and make the app friendlier for everyone.

Docs, Docs, Docs

I’m a big believer that good documentation is just as important as good code. I updated the docs to cover all the new features, added citations for the privacy metrics, and made the install process a bit more straightforward. Hopefully, this means new users and contributors can get up to speed without too much hassle.

Huge Thanks to My Mentors and the Team

I really want to shine a light on Dr. Bez, Prof. Byna, and the entire AIDRIN team here. Their encouragement, practical advice, and collaborative spirit have been a huge part of my progress. Whether I’m stuck on a bug, brainstorming a new feature, or just need a second opinion, there’s always someone ready to help me think things through. Their experience and support have shaped not just the technical side of my work, but also how I approach problem-solving and teamwork.

What’s Next?

Looking ahead, I’m planning to expand AIDRIN’s support for multimodal datasets and keep refining the privacy and fairness modules. There’s always something new to learn or improve, and I’m excited to keep building. If you’re interested in data quality, privacy, or open-source AI tools, I’d love to connect and swap ideas.

Thanks for reading and for following along with my GSoC journey. I’ll be back soon with more updates!

This is the second post in my 3-part GSoC series with AIDRIN. Stay tuned for the final update.

Introducing Scenic-RoboSuite Interface

Sun, 15 Jun 2025 00:00:00 +0000

Hey! I’m Sahil, working on integrating Scenic with RoboSuite for GSoC 2025. My project is mentored by Daniel Fremont and Eric Vin .

I’m connecting Scenic (a probabilistic programming language for scenarios) with RoboSuite (a robotics simulation framework). Basically, you write simple scenario descriptions and get complex 3D robot simulations automatically.

Currently, as I’m building things and learning how Scenic works, I have been able to get the basic skeleton for the simulator interface working. I’ve implemented the simulator class and built a world model that can translate Scenic objects into RoboSuite’s simulator (which is MuJoCo-based). The interface now handles precise object placement in the world pretty well.

One of the trickier parts was figuring out the translation logic between Scenic and RoboSuite. I managed to overcome this by building a system that automatically detects the shape of objects when moving between the two frameworks, which lays a foundation for more complex object mapping later on.

I’ve also built some basic example scenarios to run and test with. Currently working on more complex examples and testing Scenic’s features like probabilistic object placement, constraint satisfaction, and spatial relationships between objects.

In summary, the “Scenic to RoboSuite” part of the interface is pretty much done. For next week, I need to work on the “RoboSuite to Scenic” part - basically getting feedback and state information flowing back from the simulation. Achieving this will make a complete bridge and give us a working simulator interface, which is the first major milestone for the project.

Improving AI Data Pipelines in AIDRIN: A Privacy-Centric and Multimodal Expansion

Thu, 12 Jun 2025 00:00:00 +0000

⏱️ Reading time: 4–5 minutes

Hi 👋

I’m Harish Balaji, a Master’s student at NYU with a focus on Artificial Intelligence, Machine Learning, and Cybersecurity. I’m especially interested in building scalable systems that reflect responsible AI principles. For me, data quality isn’t just a technical detail. It’s a foundational aspect of building models that are reliable, fair, and reproducible in the real world.

This summer, I’m contributing to AIDRIN (AI Data Readiness Inspector) as part of Google Summer of Code 2025. I’m grateful to be working under the mentorship of Dr. Jean Luca Bez and Prof. Suren Byna from the Scientific Data Management Group at Lawrence Berkeley National Laboratory (LBNL).

AIDRIN is an open-source framework that helps researchers and practitioners evaluate whether a dataset is truly ready to be used in production-level AI workflows. From fairness to privacy, it provides a structured lens through which we can understand the strengths and gaps in our data.

Why this work matters

In machine learning, one principle always holds true:

“Garbage in, garbage out.”

Even the most advanced models can underperform or amplify harmful biases if trained on incomplete, imbalanced, or poorly understood data. This is where AIDRIN steps in. It provides practical tools to assess datasets across key dimensions like privacy, fairness, class balance, interpretability, and support for multiple modalities.

By making these characteristics measurable and transparent, AIDRIN empowers teams to make informed decisions early in the pipeline. It helps ensure that datasets are not only large or complex, but also trustworthy, representative, and purpose-fit.

My focus this summer

As part of my GSoC 2025 project, I’ll be focusing on extending AIDRIN’s evaluation capabilities. A big part of this involves strengthening its support for privacy metrics and designing tools that can handle non-tabular datasets, such as image-based data.

The goal is to expand AIDRIN’s reach without compromising on interpretability or ease of use. More technical insights and updates will follow in the next posts as the summer progresses.

What comes next

As the AI community continues to evolve, there’s a growing shift toward data-centric practices. I believe frameworks like AIDRIN are essential for helping us move beyond the question of “Does the model work?” toward a deeper and more meaningful one: “Was the data ready in the first place?”

Over the next few weeks, I’ll be working on development, testing, and integration. I’m excited to contribute to a tool that emphasizes transparency and reproducibility across the AI lifecycle, and to share lessons and ideas with others who care about responsible AI.

If you’re exploring similar challenges or working in the space of dataset evaluation and readiness, I’d love to connect and exchange thoughts. You can also read my full GSoC 2025 proposal below for more context around the project scope and vision:

👉 Read my GSoC 2025 proposal here

This is the first in a 3-part blog series documenting my GSoC journey with AIDRIN. Stay tuned for technical updates and behind-the-scenes insights as the summer unfolds!